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Storage-compute disaggregation has recently emerged as a novel architecture in modern data centers, particularly in the cloud. By decoupling compute from storage, this new architecture enables independent and elastic scaling of compute and storage resources, potentially increasing resource utilization and reducing overall costs. To best leverage the disaggregated architecture, a new breed of database systems termed storage-disaggregated databases has recently been developed, such as Amazon Aurora, Microsoft Socrates, Google AlloyDB, Alibaba PolarDB, and Huawei Taurus. However, little is known about the effectiveness of the design principles in these databases since they are typically developed by industry giants, and only the overall performance results are presented without detailing the impact of individual design principles. As a result, many critical research questions remain unclear, such as the performance impact of storage-disaggregation, the log-as-the-database design, shared-storage, and various log-replay methods. In this paper, we investigate the performance implications of the design principles that are widely adopted in storage-disaggregated databases for the first time. As these databases were usually not open-sourced, we have made a significant effort to implement a storage-disaggregated database prototype based on PostgreSQL v13.0. By fully controlling and instrumenting the codebase, we are able to selectively enable and disable individual optimizations and techniques to evaluate their impact on performance in various scenarios. Furthermore, we open-source our storage-disaggregated database prototype for use by the broader database research community, fostering collaboration and innovation in this field.
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Disaggregation: A New Architecture for Cloud Databases
Disaggregation—the separation of database components into independently managed and scalable services—has emerged as a foundational architecture for cloud-native databases. It enables key benefits such as elasticity, resource pooling, and cost efficiency. This paper offers a perspective on the disaggregation trend, tracing its evolution, and presents a set of research efforts that redesign and optimize distributed databases in this new architecture. Finally, the paper outlines future directions and open challenges, highlighting disaggregation as a rich and still largely unexplored area for database research.
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- Award ID(s):
- 2144588
- PAR ID:
- 10687781
- Publisher / Repository:
- VLDB
- Date Published:
- Journal Name:
- Proceedings of the VLDB Endowment
- Volume:
- 18
- Issue:
- 12
- ISSN:
- 2150-8097
- Page Range / eLocation ID:
- 5527 to 5530
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.14778/3750601.3760520
